MarkSfik commented on a change in pull request #438:
URL: https://github.com/apache/flink-web/pull/438#discussion_r624988258
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File path: _posts/2021-04-22-release-1.13.0.md
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+---
+layout: post
+title: "Apache Flink 1.13.0 Release Announcement"
+date: 2021-04-22T08:00:00.000Z
+categories: news
+authors:
+- stephan:
+ name: "Stephan Ewen"
+ twitter: "StephanEwen"
+- dwysakowicz:
+ name: "Dawid Wysakowicz"
+ twitter: "dwysakowicz"
+
+excerpt: The Apache Flink community is excited to announce the release of
Flink 1.13.0! Around 200 contributors worked on over 1,000 issues to bring
significant improvements to usability and observability as well as new features
that improve elasticity of Flink’s Application-style deployments.
+---
+
+
+The Apache Flink community is excited to announce the release of Flink 1.13.0!
More than 200
+contributors worked on over 1,000 issues for this new version.
+
+The release brings us a big step forward in one of our major efforts: **Making
Stream Processing
+Applications as natural and as simple to manage as any other application.**
The new *reactive scaling*
+mode means that scaling streaming applications in and out now works like in
any other application,
+by just changing the number of parallel processes.
+
+The release also prominently features a **series of improvements that help
users better understand the performance of
+applications.** When the streams don't flow as fast as you'd hope, these can
help you to understand
+why: Load and *backpressure visualization* to identify bottlenecks, *CPU flame
graphs* to identify hot
+code paths in your application, and *State Access Latencies* to see how the
State Backends are keeping
+up.
+
+Beyond those features, the Flink community has added a ton of improvements all
over the system,
+some of which we discuss in this article. We hope you enjoy the new release
and features.
+Towards the end of the article, we describe changes to be aware of when
upgrading
+from earlier versions of Apache Flink.
+
+{% toc %}
+
+We encourage you to [download the
release](https://flink.apache.org/downloads.html) and share your
+feedback with the community through
+the [Flink mailing
lists](https://flink.apache.org/community.html#mailing-lists)
+or [JIRA](https://issues.apache.org/jira/projects/FLINK/summary).
+
+----
+
+# Notable Features
+
+## Reactive Scaling
+
+Reactive Scaling is the latest piece in Flink's initiative to make Stream
Processing
+Applications as natural and as simple to manage as any other application.
+
+Flink has a dual nature when it comes to resource management and deployments:
You can deploy
+Flink applications onto resource orchestrators like Kubernetes or Yarn in such
a way that Flink actively manages
+the resources, and allocates and releases workers as needed. That is
especially useful for jobs and
+applications that rapidly change their required resources, like batch
applications and ad-hoc SQL
+queries. The application parallelism rules, the number of workers follows. In
the context of Flink
+applications, we call this *active scaling*.
+
+For long running streaming applications, it is often a nicer model to just
deploy them like any
+other long-running application: The application doesn't really need to know
that it runs on K8s,
+EKS, Yarn, etc. and doesn't try to acquire a specific amount of workers;
instead, it just uses the
+number of workers that is given to it. The number of workers rules, the
application parallelism
+adjusts to that. In the context of Flink, we call that *re-active scaling*.
+
+The [Application Deployment Mode]({{ site.DOCS_BASE_URL
}}flink-docs-release-1.13/docs/concepts/flink-architecture/#flink-application-execution)
+started this effort, making deployments more application-like (by avoiding two
separate deployment
+steps to (1) start cluster and (2) submit application). The reactive scaling
mode completes this,
+and you now don't have to use extra tools (scripts or a K8s operator) any more
to keep the number
+of workers and the application parallelism settings in sync.
+
+You can now put an auto-scaler around Flink applications like around other
typical applications — as
+long as you are mindful about the cost of rescaling, when configuring the
autoscaler: Stateful
+streaming applications must move state around when scaling.
+
+To try the reactive-scaling mode, add the `scheduler-mode: reactive` config
entry and deploy
+an application cluster ([standalone]({{ site.DOCS_BASE_URL
}}flink-docs-release-1.13/docs/deployment/resource-providers/standalone/overview/#application-mode)
or [Kubernetes]({{ site.DOCS_BASE_URL
}}flink-docs-release-1.13/docs/deployment/resource-providers/standalone/kubernetes/#deploy-application-cluster)).
Check out [the reactive scaling docs]({{ site.DOCS_BASE_URL
}}flink-docs-release-1.13/docs/deployment/elastic_scaling/#reactive-mode) for
more details.
+
+
+## Analyzing Application Performance
+
+Like for any application, analyzing and understanding the performance of a
Flink application
+is critical. Often event more critical, because Flink applications are
typically data-intensive
+(process high volumes of data) and are at the same time expected to provide
results within
+(near-) real-time latencies.
+
+When an application doesn't keep up with the data rate any more, or an
application takes more
+resources than you'd expect it would, these new tools can help you track down
the causes:
+
+**Bottleneck detection, Back Pressure Monitoring**
+
+The first question during performance analysis is often: Which operation is
the bottleneck?
+
+To help answer that, Flink exposes metrics about the degree to which tasks are
*busy* (doing work)
+and *back-pressured* (have capacity to do work, but cannot, because their
successor operators
+are cannot accept more results). Candidates for bottlenecks are the busy
operators whose predecessors
Review comment:
```suggestion cannot accept more results). Candidates for bottlenecks
are the busy operators whose predecessors
```
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